{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This demo shows how to use integrated gradients in graph convolutional networks to obtain accurate importance estimations for both the nodes and edges. The notebook consists of three parts:\n",
    "- setting up the node classification problem for Cora citation network\n",
    "- training and evaluating a GCN model for node classification\n",
    "- calculating node and edge importances for model's predictions of query (\"target\") nodes\n",
    "\n",
    "<a name=\"refs\"></a>\n",
    "**References**\n",
    "\n",
    "[1] Axiomatic Attribution for Deep Networks. M. Sundararajan, A. Taly, and Q. Yan.\n",
    "    Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, PMLR 70, 2017\n",
    "    ([link](https://arxiv.org/pdf/1703.01365.pdf)).\n",
    "    \n",
    "[2] Adversarial Examples on Graph Data: Deep Insights into Attack and Defense. H. Wu, C. Wang, Y. Tyshetskiy, A. Docherty, K. Lu, and L. Zhu. arXiv: 1903.01610 ([link](https://arxiv.org/abs/1903.01610)).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import os\n",
    "import time\n",
    "import stellargraph as sg\n",
    "from stellargraph.mapper import FullBatchNodeGenerator\n",
    "from stellargraph.layer import GCN\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers, optimizers, losses, metrics, Model, regularizers\n",
    "from sklearn import preprocessing, feature_extraction, model_selection\n",
    "from copy import deepcopy\n",
    "import matplotlib.pyplot as plt\n",
    "from stellargraph import datasets\n",
    "from IPython.display import display, HTML\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the CORA network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "The Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = datasets.Cora()\n",
    "display(HTML(dataset.description))\n",
    "dataset.download()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the graph from edgelist (in `cited-paper` <- `citing-paper` order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "edgelist = pd.read_csv(\n",
    "    os.path.join(dataset.data_directory, \"cora.cites\"),\n",
    "    sep=\"\\t\",\n",
    "    header=None,\n",
    "    names=[\"target\", \"source\"],\n",
    ")\n",
    "edgelist[\"label\"] = \"cites\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Gnx = nx.from_pandas_edgelist(edgelist, edge_attr=\"label\")\n",
    "nx.set_node_attributes(Gnx, \"paper\", \"label\")\n",
    "feature_names = [\"w_{}\".format(ii) for ii in range(1433)]\n",
    "column_names = feature_names + [\"subject\"]\n",
    "node_data = pd.read_csv(\n",
    "    os.path.join(dataset.data_directory, \"cora.content\"),\n",
    "    sep=\"\\t\",\n",
    "    header=None,\n",
    "    names=column_names,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The adjacency matrix is ordered by the graph nodes from G.nodes(). To make computations easier, let's reindex the node data to maintain the same ordering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph_nodes = list(Gnx.nodes())\n",
    "node_data = node_data.loc[graph_nodes]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For machine learning we want to take a subset of the nodes for training, and use the rest for validation and testing. We'll use scikit-learn again to do this.\n",
    "\n",
    "Here we're taking 140 node labels for training, 500 for validation, and the rest for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data, test_data = model_selection.train_test_split(\n",
    "    node_data, train_size=140, test_size=None, stratify=node_data[\"subject\"]\n",
    ")\n",
    "val_data, test_data = model_selection.train_test_split(\n",
    "    test_data, train_size=500, test_size=None, stratify=test_data[\"subject\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting to numeric arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our categorical target, we will use one-hot vectors that will be fed into a soft-max Keras layer during training. To do this conversion ...\n",
    "\n",
    "We now do the same for the node attributes we want to use to predict the subject. These are the feature vectors that the Keras model will use as input. The CORA dataset contains attributes 'w_x' that correspond to words found in that publication. If a word occurs more than once in a publication the relevant attribute will be set to one, otherwise it will be zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_encoding = feature_extraction.DictVectorizer(sparse=False)\n",
    "feature_names = [\"w_{}\".format(ii) for ii in range(1433)]\n",
    "column_names = feature_names + [\"subject\"]\n",
    "train_targets = target_encoding.fit_transform(train_data[[\"subject\"]].to_dict(\"records\"))\n",
    "val_targets = target_encoding.transform(val_data[[\"subject\"]].to_dict(\"records\"))\n",
    "test_targets = target_encoding.transform(test_data[[\"subject\"]].to_dict(\"records\"))\n",
    "\n",
    "node_ids = node_data.index\n",
    "all_targets = target_encoding.transform(node_data[[\"subject\"]].to_dict(\"records\"))\n",
    "node_features = node_data[feature_names]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the GCN model in Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now create a StellarGraph object from the NetworkX graph and the node features and targets. To feed data from the graph to the Keras model we need a generator. Since GCN is a full-batch model, we use the `FullBatchNodeGenerator` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using GCN (local pooling) filters...\n"
     ]
    }
   ],
   "source": [
    "G = sg.StellarGraph(Gnx, node_features=node_features)\n",
    "generator = FullBatchNodeGenerator(G, sparse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For training we map only the training nodes returned from our splitter and the target values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_gen = generator.flow(train_data.index, train_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can specify our machine learning model: tn this example we use two GCN layers with 16-dimensional hidden node features at each layer with ELU activation functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "layer_sizes = [16, 16]\n",
    "gcn = GCN(\n",
    "    layer_sizes=layer_sizes,\n",
    "    activations=[\"elu\", \"elu\"],\n",
    "    generator=generator,\n",
    "    dropout=0.3,\n",
    "    kernel_regularizer=regularizers.l2(5e-4),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Expose the input and output tensors of the GCN model for node prediction, via GCN.build() method:\n",
    "x_inp, x_out = gcn.build()\n",
    "# Snap the final estimator layer to x_out\n",
    "x_out = layers.Dense(units=train_targets.shape[1], activation=\"softmax\")(x_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create the actual Keras model with the input tensors `x_inp` and output tensors being the predictions `x_out` from the final dense layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Model(inputs=x_inp, outputs=x_out)\n",
    "\n",
    "model.compile(\n",
    "    optimizer=optimizers.Adam(lr=0.01),  # decay=0.001),\n",
    "    loss=losses.categorical_crossentropy,\n",
    "    metrics=[metrics.categorical_accuracy],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model, keeping track of its loss and accuracy on the training set, and its generalisation performance on the validation set (we need to create another generator over the validation data for this)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_gen = generator.flow(val_data.index, val_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1/1 - 0s - loss: 1.9683 - categorical_accuracy: 0.1643 - val_loss: 1.8283 - val_categorical_accuracy: 0.4700\n",
      "Epoch 2/20\n",
      "1/1 - 0s - loss: 1.7976 - categorical_accuracy: 0.5429 - val_loss: 1.7170 - val_categorical_accuracy: 0.4580\n",
      "Epoch 3/20\n",
      "1/1 - 0s - loss: 1.6227 - categorical_accuracy: 0.6071 - val_loss: 1.6138 - val_categorical_accuracy: 0.4540\n",
      "Epoch 4/20\n",
      "1/1 - 0s - loss: 1.4720 - categorical_accuracy: 0.5929 - val_loss: 1.5156 - val_categorical_accuracy: 0.4860\n",
      "Epoch 5/20\n",
      "1/1 - 0s - loss: 1.3223 - categorical_accuracy: 0.6357 - val_loss: 1.4217 - val_categorical_accuracy: 0.5240\n",
      "Epoch 6/20\n",
      "1/1 - 0s - loss: 1.1646 - categorical_accuracy: 0.6500 - val_loss: 1.3291 - val_categorical_accuracy: 0.5760\n",
      "Epoch 7/20\n",
      "1/1 - 0s - loss: 1.0347 - categorical_accuracy: 0.7571 - val_loss: 1.2374 - val_categorical_accuracy: 0.6260\n",
      "Epoch 8/20\n",
      "1/1 - 0s - loss: 0.9030 - categorical_accuracy: 0.8286 - val_loss: 1.1490 - val_categorical_accuracy: 0.7080\n",
      "Epoch 9/20\n",
      "1/1 - 0s - loss: 0.7875 - categorical_accuracy: 0.8786 - val_loss: 1.0695 - val_categorical_accuracy: 0.7620\n",
      "Epoch 10/20\n",
      "1/1 - 0s - loss: 0.6504 - categorical_accuracy: 0.9571 - val_loss: 1.0020 - val_categorical_accuracy: 0.7960\n",
      "Epoch 11/20\n",
      "1/1 - 0s - loss: 0.6027 - categorical_accuracy: 0.9571 - val_loss: 0.9465 - val_categorical_accuracy: 0.8040\n",
      "Epoch 12/20\n",
      "1/1 - 0s - loss: 0.5317 - categorical_accuracy: 0.9714 - val_loss: 0.9013 - val_categorical_accuracy: 0.8060\n",
      "Epoch 13/20\n",
      "1/1 - 0s - loss: 0.4588 - categorical_accuracy: 0.9714 - val_loss: 0.8642 - val_categorical_accuracy: 0.8000\n",
      "Epoch 14/20\n",
      "1/1 - 0s - loss: 0.4223 - categorical_accuracy: 0.9714 - val_loss: 0.8359 - val_categorical_accuracy: 0.8020\n",
      "Epoch 15/20\n",
      "1/1 - 0s - loss: 0.3641 - categorical_accuracy: 0.9786 - val_loss: 0.8166 - val_categorical_accuracy: 0.8040\n",
      "Epoch 16/20\n",
      "1/1 - 0s - loss: 0.3240 - categorical_accuracy: 0.9929 - val_loss: 0.8061 - val_categorical_accuracy: 0.7980\n",
      "Epoch 17/20\n",
      "1/1 - 0s - loss: 0.3008 - categorical_accuracy: 0.9857 - val_loss: 0.8050 - val_categorical_accuracy: 0.7920\n",
      "Epoch 18/20\n",
      "1/1 - 0s - loss: 0.2759 - categorical_accuracy: 0.9786 - val_loss: 0.8096 - val_categorical_accuracy: 0.7900\n",
      "Epoch 19/20\n",
      "1/1 - 0s - loss: 0.2628 - categorical_accuracy: 0.9929 - val_loss: 0.8143 - val_categorical_accuracy: 0.7820\n",
      "Epoch 20/20\n",
      "1/1 - 0s - loss: 0.2564 - categorical_accuracy: 0.9929 - val_loss: 0.8168 - val_categorical_accuracy: 0.7860\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(\n",
    "    train_gen, shuffle=False, epochs=20, verbose=2, validation_data=val_gen\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "def remove_prefix(text, prefix):\n",
    "    return text[text.startswith(prefix) and len(prefix):]\n",
    "\n",
    "def plot_history(history):\n",
    "    metrics = sorted(set([remove_prefix(m, \"val_\") for m in list(history.history.keys())]))\n",
    "    for m in metrics:\n",
    "        # summarize history for metric m\n",
    "        plt.plot(history.history[m])\n",
    "        plt.plot(history.history['val_' + m])\n",
    "        plt.title(m)\n",
    "        plt.ylabel(m)\n",
    "        plt.xlabel('epoch')\n",
    "        plt.legend(['train', 'validation'], loc='best')\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate the trained model on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test Set Metrics:\n",
      "\tloss: 0.7801\n",
      "\tcategorical_accuracy: 0.8056\n"
     ]
    }
   ],
   "source": [
    "test_gen = generator.flow(test_data.index, test_targets)\n",
    "test_metrics = model.evaluate_generator(test_gen)\n",
    "print(\"\\nTest Set Metrics:\")\n",
    "for name, val in zip(model.metrics_names, test_metrics):\n",
    "    print(\"\\t{}: {:0.4f}\".format(name, val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Node and link importance via saliency maps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to understand why a selected node is predicted as a certain class we want to find the node feature importance, total node importance, and link importance for nodes and edges in the selected node's neighbourhood (ego-net). These importances give information about the effect of changes in the node's features and its neighbourhood on the prediction of the node, specifically:\n",
    "\n",
    "#### Node feature importance:\n",
    "Given the selected node $t$ and the model's prediction $s(c)$ for class $c$. The feature importance can be calculated for each node $v$ in the selcted node's ego-net where the importance of feature $f$ for node $v$ is the change predicted score $s(c)$ for the selected node when the feature $f$ of node $v$ is perturbed.\n",
    "\n",
    "#### Total node importance:\n",
    "This is defined as the sum of the feature importances for node $v$ for all features. Nodes with high importance (positive or negative) affect the prediction for the selected node more than links with low importance. \n",
    "\n",
    "#### Link importance:\n",
    "This is defined as the change in the selected node's predicted score $s(c)$ if the link $e=(u, v)$ is removed from the graph. Links with high importance (positive or negative) affect the prediction for the selected node more than links with low importance. \n",
    "\n",
    "Node and link importances can be used to assess the role of nodes and links in model's predictions for the node(s) of interest (the selected node). For datasets like CORA-ML, the features and edges are binary, vanilla gradients may not perform well so we use integrated gradients [[1]](#refs) to compute them.\n",
    "\n",
    "Another interesting application of node and link importances is to identify model vulnerabilities to attacks via perturbing node features and graph structure (see [[2]](#refs))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To investigate these importances we use the StellarGraph `saliency_maps` routines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from stellargraph.utils.saliency_maps import IntegratedGradients, GradientSaliency"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select the target node whose prediction is to be interpreted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_idx = 7  # index of the selected node in G.nodes()\n",
    "target_nid = graph_nodes[target_idx]  # Node ID of the selected node\n",
    "y_true = all_targets[target_idx]  # true class of the target node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected node id: 1109199, \n",
      "True label: [0. 1. 0. 0. 0. 0. 0.], \n",
      "Predicted scores: [0.11 0.6  0.07 0.1  0.07 0.02 0.03]\n"
     ]
    }
   ],
   "source": [
    "all_gen = generator.flow(graph_nodes)\n",
    "y_pred = model.predict_generator(all_gen)[0, target_idx]\n",
    "class_of_interest = np.argmax(y_pred)\n",
    "\n",
    "print(\n",
    "    \"Selected node id: {}, \\nTrue label: {}, \\nPredicted scores: {}\".format(\n",
    "        target_nid, y_true, y_pred.round(2)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the node feature importance by using integrated gradients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "int_grad_saliency = IntegratedGradients(model, train_gen)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the parameters of `get_node_importance` method, `X` and `A` are the feature and adjacency matrices, respectively. If `sparse` option is enabled, `A` will be the non-zero values of the adjacency matrix with `A_index` being the indices. `target_idx` is the node of interest, and `class_of_interest` is set as the predicted label of the node. `steps` indicates the number of steps used to approximate the integration in integrated gradients calculation. A larger value of `steps` gives better approximation, at the cost of higher computational overhead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "integrated_node_importance = int_grad_saliency.get_node_importance(\n",
    "    target_idx, class_of_interest, steps=50\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2708,)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "integrated_node_importance.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "integrated_node_importance [ 0.06  3.37 -0.02 ...  0.    0.    0.  ]\n",
      "integrate_node_importance.shape = (2708,)\n",
      "integrated self-importance of target node 1109199: -2.58\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nintegrated_node_importance\", integrated_node_importance.round(2))\n",
    "print(\"integrate_node_importance.shape = {}\".format(integrated_node_importance.shape))\n",
    "print(\n",
    "    \"integrated self-importance of target node {}: {}\".format(\n",
    "        target_nid, integrated_node_importance[target_idx].round(2)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that number of non-zero node importance values is less or equal the number of nodes in target node's K-hop ego net (where K is the number of GCN layers in the model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "G_ego = nx.ego_graph(Gnx, target_nid, radius=len(gcn.activations))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes in the ego graph: 202\n",
      "Number of non-zero elements in integrated_node_importance: 202\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of nodes in the ego graph: {}\".format(len(G_ego.nodes())))\n",
    "print(\n",
    "    \"Number of non-zero elements in integrated_node_importance: {}\".format(\n",
    "        np.count_nonzero(integrated_node_importance)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now compute the link importance using integrated gradients [[1]](#refs). Integrated gradients are obtained by cumulating the gradients along the path between the baseline (all-zero graph) and the state of the graph. They provide better sensitivity for the graphs with binary features and edges compared with the vanilla gradients. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "integrate_link_importance = int_grad_saliency.get_integrated_link_masks(\n",
    "    target_idx, class_of_interest, steps=50\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "integrate_link_importance.shape = (2708, 2708)\n",
      "Number of non-zero elements in integrate_link_importance: 210\n"
     ]
    }
   ],
   "source": [
    "integrate_link_importance_dense = np.array(integrate_link_importance.todense())\n",
    "print(\"integrate_link_importance.shape = {}\".format(integrate_link_importance.shape))\n",
    "print(\n",
    "    \"Number of non-zero elements in integrate_link_importance: {}\".format(\n",
    "        np.count_nonzero(integrate_link_importance.todense())\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now find the nodes that have the highest importance to the prediction of the selected node:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 most important links by integrated gradients are:\n",
      " [(7, 58), (7, 1), (58, 1806), (58, 58), (7, 7), (58, 1), (223, 982), (1, 92), (1, 91), (1, 125)]\n"
     ]
    }
   ],
   "source": [
    "sorted_indices = np.argsort(integrate_link_importance_dense.flatten())\n",
    "N = len(graph_nodes)\n",
    "integrated_link_importance_rank = [(k // N, k % N) for k in sorted_indices[::-1]]\n",
    "topk = 10\n",
    "# integrate_link_importance = integrate_link_importance_dense\n",
    "print(\n",
    "    \"Top {} most important links by integrated gradients are:\\n {}\".format(\n",
    "        topk, integrated_link_importance_rank[:topk]\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the labels as an attribute for the nodes in the graph. The labels are used to color the nodes in different classes.\n",
    "nx.set_node_attributes(\n",
    "    G_ego, values={x[0]: {\"subject\": x[1]} for x in node_data[\"subject\"].items()}\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following, we plot the link and node importance (computed by integrated gradients) of the nodes within the ego graph of the target node. \n",
    "\n",
    "For nodes, the shape of the node indicates the positive/negative importance the node has. 'round' nodes have positive importance while 'diamond' nodes have negative importance. The size of the node indicates the value of the importance, e.g., a large diamond node has higher negative importance. \n",
    "\n",
    "For links, the color of the link indicates the positive/negative importance the link has. 'red' links have positive importance while 'blue' links have negative importance. The width of the link indicates the value of the importance, e.g., a thicker blue link has higher negative importance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.459022005670704"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "integrated_node_importance.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.17307626384776087"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "integrate_link_importance.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "node_size_factor = 1e2\n",
    "link_width_factor = 2\n",
    "\n",
    "nodes = list(G_ego.nodes())\n",
    "colors = pd.DataFrame(\n",
    "    [v[1][\"subject\"] for v in G_ego.nodes(data=True)], index=nodes, columns=[\"subject\"]\n",
    ")\n",
    "colors = np.argmax(target_encoding.transform(colors.to_dict(\"records\")), axis=1) + 1\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(15, 10))\n",
    "pos = nx.spring_layout(G_ego)\n",
    "\n",
    "# Draw ego as large and red\n",
    "node_sizes = [integrated_node_importance[graph_nodes.index(k)] for k in nodes]\n",
    "node_shapes = [\"o\" if w > 0 else \"d\" for w in node_sizes]\n",
    "\n",
    "positive_colors, negative_colors = [], []\n",
    "positive_node_sizes, negative_node_sizes = [], []\n",
    "positive_nodes, negative_nodes = [], []\n",
    "node_size_scale = node_size_factor / np.max(node_sizes)\n",
    "for k in range(len(nodes)):\n",
    "    if nodes[k] == target_idx:\n",
    "        continue\n",
    "    if node_shapes[k] == \"o\":\n",
    "        positive_colors.append(colors[k])\n",
    "        positive_nodes.append(nodes[k])\n",
    "        positive_node_sizes.append(node_size_scale * node_sizes[k])\n",
    "\n",
    "    else:\n",
    "        negative_colors.append(colors[k])\n",
    "        negative_nodes.append(nodes[k])\n",
    "        negative_node_sizes.append(node_size_scale * abs(node_sizes[k]))\n",
    "\n",
    "# Plot the ego network with the node importances\n",
    "cmap = plt.get_cmap(\"jet\", np.max(colors) - np.min(colors) + 1)\n",
    "nc = nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=positive_nodes,\n",
    "    node_color=positive_colors,\n",
    "    cmap=cmap,\n",
    "    node_size=positive_node_sizes,\n",
    "    with_labels=False,\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    node_shape=\"o\",\n",
    ")\n",
    "nc = nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=negative_nodes,\n",
    "    node_color=negative_colors,\n",
    "    cmap=cmap,\n",
    "    node_size=negative_node_sizes,\n",
    "    with_labels=False,\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    node_shape=\"d\",\n",
    ")\n",
    "# Draw the target node as a large star colored by its true subject\n",
    "nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=[target_nid],\n",
    "    node_size=50 * abs(node_sizes[nodes.index(target_nid)]),\n",
    "    node_shape=\"*\",\n",
    "    node_color=[colors[nodes.index(target_nid)]],\n",
    "    cmap=cmap,\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    label=\"Target\",\n",
    ")\n",
    "\n",
    "# Draw the edges with the edge importances\n",
    "edges = G_ego.edges()\n",
    "weights = [\n",
    "    integrate_link_importance[graph_nodes.index(u), graph_nodes.index(v)]\n",
    "    for u, v in edges\n",
    "]\n",
    "edge_colors = [\"red\" if w > 0 else \"blue\" for w in weights]\n",
    "weights = link_width_factor * np.abs(weights) / np.max(weights)\n",
    "\n",
    "ec = nx.draw_networkx_edges(G_ego, pos, edge_color=edge_colors, width=weights)\n",
    "plt.legend()\n",
    "plt.colorbar(nc, ticks=np.arange(np.min(colors), np.max(colors) + 1))\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then remove the node or edge in the ego graph one by one and check how the prediction changes. By doing so, we can obtain the ground truth importance of the nodes and edges. Comparing the following figure and the above one can show the effectiveness of integrated gradients as the importance approximations are relatively consistent with the ground truth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X, _, A_index, A), _ = train_gen[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_bk = deepcopy(X)\n",
    "A_bk = deepcopy(A)\n",
    "selected_nodes = np.array([[target_idx]], dtype=\"int32\")\n",
    "nodes = [graph_nodes.index(v) for v in G_ego.nodes()]\n",
    "edges = [(graph_nodes.index(u), graph_nodes.index(v)) for u, v in G_ego.edges()]\n",
    "clean_prediction = model.predict([X, selected_nodes, A_index, A]).squeeze()\n",
    "predict_label = np.argmax(clean_prediction)\n",
    "\n",
    "groud_truth_node_importance = np.zeros((N,))\n",
    "for node in nodes:\n",
    "    # we set all the features of the node to zero to check the ground truth node importance.\n",
    "    X_perturb = deepcopy(X_bk)\n",
    "    X_perturb[:, node, :] = 0\n",
    "    predict_after_perturb = model.predict(\n",
    "        [X_perturb, selected_nodes, A_index, A]\n",
    "    ).squeeze()\n",
    "    groud_truth_node_importance[node] = (\n",
    "        clean_prediction[predict_label] - predict_after_perturb[predict_label]\n",
    "    )\n",
    "\n",
    "node_shapes = [\n",
    "    \"o\" if groud_truth_node_importance[k] > 0 else \"d\" for k in range(len(nodes))\n",
    "]\n",
    "positive_colors, negative_colors = [], []\n",
    "positive_node_sizes, negative_node_sizes = [], []\n",
    "positive_nodes, negative_nodes = [], []\n",
    "# node_size_scale is used for better visulization of nodes\n",
    "node_size_scale = node_size_factor / max(groud_truth_node_importance)\n",
    "\n",
    "for k in range(len(node_shapes)):\n",
    "    if nodes[k] == target_idx:\n",
    "        continue\n",
    "    if node_shapes[k] == \"o\":\n",
    "        positive_colors.append(colors[k])\n",
    "        positive_nodes.append(graph_nodes[nodes[k]])\n",
    "        positive_node_sizes.append(\n",
    "            node_size_scale * groud_truth_node_importance[nodes[k]]\n",
    "        )\n",
    "    else:\n",
    "        negative_colors.append(colors[k])\n",
    "        negative_nodes.append(graph_nodes[nodes[k]])\n",
    "        negative_node_sizes.append(\n",
    "            node_size_scale * abs(groud_truth_node_importance[nodes[k]])\n",
    "        )\n",
    "X = deepcopy(X_bk)\n",
    "groud_truth_edge_importance = np.zeros((N, N))\n",
    "G_edge_indices = [(A_index[0, k, 0], A_index[0, k, 1]) for k in range(A.shape[1])]\n",
    "\n",
    "for edge in edges:\n",
    "    edge_index = G_edge_indices.index((edge[0], edge[1]))\n",
    "    origin_val = A[0, edge_index]\n",
    "\n",
    "    A[0, edge_index] = 0\n",
    "    # we set the weight of a given edge to zero to check the ground truth link importance\n",
    "    predict_after_perturb = model.predict([X, selected_nodes, A_index, A]).squeeze()\n",
    "    groud_truth_edge_importance[edge[0], edge[1]] = (\n",
    "        predict_after_perturb[predict_label] - clean_prediction[predict_label]\n",
    "    ) / (0 - 1)\n",
    "    A[0, edge_index] = origin_val\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(15, 10))\n",
    "cmap = plt.get_cmap(\"jet\", np.max(colors) - np.min(colors) + 1)\n",
    "# Draw the target node as a large star colored by its true subject\n",
    "nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=[target_nid],\n",
    "    node_size=50 * abs(node_sizes[nodes.index(target_idx)]),\n",
    "    node_color=[colors[nodes.index(target_idx)]],\n",
    "    cmap=cmap,\n",
    "    node_shape=\"*\",\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    label=\"Target\",\n",
    ")\n",
    "# Draw the ego net\n",
    "nc = nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=positive_nodes,\n",
    "    node_color=positive_colors,\n",
    "    cmap=cmap,\n",
    "    node_size=positive_node_sizes,\n",
    "    with_labels=False,\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    node_shape=\"o\",\n",
    ")\n",
    "nc = nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=negative_nodes,\n",
    "    node_color=negative_colors,\n",
    "    cmap=cmap,\n",
    "    node_size=negative_node_sizes,\n",
    "    with_labels=False,\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    node_shape=\"d\",\n",
    ")\n",
    "edges = G_ego.edges()\n",
    "weights = [\n",
    "    groud_truth_edge_importance[graph_nodes.index(u), graph_nodes.index(v)]\n",
    "    for u, v in edges\n",
    "]\n",
    "edge_colors = [\"red\" if w > 0 else \"blue\" for w in weights]\n",
    "weights = link_width_factor * np.abs(weights) / np.max(weights)\n",
    "\n",
    "ec = nx.draw_networkx_edges(G_ego, pos, edge_color=edge_colors, width=weights)\n",
    "plt.legend()\n",
    "plt.colorbar(nc, ticks=np.arange(np.min(colors), np.max(colors) + 1))\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By comparing the above two figures, one can see that the integrated gradients are quite consistent with the brute-force approach. The main benefit of using integrated gradients is scalability. The gradient operations are very efficient to compute on deep learning frameworks with the parallism provided by GPUs. Also, integrated gradients can give the importance of individual node features, for all nodes in the graph. Achieving this by brute-force approch is often non-trivial. "
   ]
  }
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